Submissions

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Author Guidelines

Authors are invited to make a submission to this journal. All submissions will be assessed by an editor to determine whether they meet the aims and scope of this journal. Those considered to be a good fit will be sent for peer review before determining whether they will be accepted or rejected.

Before making a submission, authors are responsible for obtaining permission to publish any material included with the submission, such as photos, documents and datasets. All authors identified on the submission must consent to be identified as an author. Where appropriate, research should be approved by an appropriate ethics committee in accordance with the legal requirements of the study's country.

An editor may desk reject a submission if it does not meet minimum standards of quality. Before submitting, please ensure that the study design and research argument are structured and articulated properly. The title should be concise and the abstract should be able to stand on its own. This will increase the likelihood of reviewers agreeing to review the paper. When you're satisfied that your submission meets this standard, please follow the checklist below to prepare your submission.

Submission Preparation Checklist

All submissions must meet the following requirements.

  • This submission meets the requirements outlined in the Author Guidelines.
  • This submission has not been previously published, nor is it before another journal for consideration.
  • All references have been checked for accuracy and completeness.
  • All tables and figures have been numbered and labeled.
  • Permission has been obtained to publish all photos, datasets and other material provided with this submission.

Learning with AI

Research in which the primary unit of analysis is the learner. Topics include cognition, metacognition, motivation, self-regulated learning, human-AI interaction and collaboration, intelligent tutoring, adaptive learning, AI chatbots as learning partners, trust and over-reliance, and learning analytics interpreted through learning theory. Submissions must examine effects on learners and learning processes, not the technical performance of AI systems. If a manuscript fits more than one section, the editor assigns it according to its primary unit of analysis.

Teaching with AI

Research in which the primary unit of analysis is the teacher or lecturer and classroom practice. Topics include instructional and curriculum design with AI, assessment and feedback, academic integrity in the age of generative AI, AI literacy and digital competence, teacher professional development, and classroom-based studies across subjects and educational levels, including faith-based and character education. Submissions must be grounded in an educational context. If a manuscript fits more than one section, the editor assigns it according to its primary unit of analysis.

Policy, Ethics, and Equity

Research in which the primary unit of analysis is the institution, education system, or society. Topics include institutional and national policy, governance of AI use, ethics, bias, privacy, transparency, data protection, equity, access, the digital divide, and cross-national or comparative perspectives. Studies of teachers' or students' views are considered here when their main findings concern policy, ethics, or equity rather than teaching practice or learning processes.

Review Articles

Systematic reviews, meta-analyses, scoping reviews, and other rigorous evidence syntheses on AI in teaching and learning. Authors must describe the search strategy, selection criteria, and analysis method. Review manuscripts are assigned to this section regardless of topic. Narrative overviews without a transparent method will not be considered.

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